1. Field of the Invention
The present invention relates to circuits for predistortion of digital signals, and particularly to a scalable digital predistortion system for linearizing a power amplifier for a radio communication transmitter.
2. Description of the Related Art
Baseband digital predistortion (DPD) implements, in the digital baseband domain, a nonlinear function that is complementary to that of the radio frequency (RF) power amplifier such that the cascaded system made of the DPD and the amplifier behaves as a linear amplification system. Even though it may seem conceptually straightforward, digital predistortion systems are quite tricky to design, since they require a perfect match between the amplifier's nonlinear behavior and the predistorter's nonlinear function. In fact, any mismatch between the two nonlinear functions will limit the performance of the DPD system and result in residual distortion. Accordingly, it is essential to understand the behavior of the power amplifier in order to design a low complexity, high performance digital predistortion system.
The power amplifiers' nonlinear behavior, which is expressed in terms of their AM/AM and AM/PM characteristics, is sensitive to the operating conditions. These conditions may vary on a long-term scale, as it is in the case of the biasing drifts and aging effects, or on a short-term scale, such as the changes in the drive signal characteristics. For a given transmission standard, the changes in the operating signal conditions mainly affect the signal's average power and/or its bandwidth. Such changes occur frequently and require a quick update of the predistorter function. However, predistorter functions usually contain a large number of coefficients for accurate linearization. In fact, in modem communication systems, wideband signals with high peak-to-average power ratio are transmitted. This emulates a dynamic, nonlinear behavior of the PA characterized by the presence of static distortion and memory effects. This requires the use of advanced digital predistorter structures that can compensate for dynamic nonlinear distortions. Several predistorter models have been reported in the literature. These include the Volterra series and their reduced versions, neural networks, memory polynomial-based predistorters, twin-nonlinear two-box structures, as well as Wiener- and Hammerstein-type predistorters. These models often result in a large number of coefficients to be identified. It is therefore important to develop scalable digital predistorter structures that can easily track changes in power amplifier behavior to ensure optimal performance.
Thus, a scalable digital predistortion system solving the aforementioned problems is desired.